
from google.colab import files

uploaded = files.upload()

Saving used_cars_data.csv to used_cars_data (1).csv


# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

#Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns




# to split the data into train and test
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

import tensorflow as tf #An end-to-end open source machine learning platform
from tensorflow import keras  # High-level neural networks API for deep learning.
from keras import backend   # Abstraction layer for neural network backend engines.
from keras.models import Sequential  # Model for building NN sequentially.
from keras.layers import Dense
# Set the seed using keras.utils.set_random_seed. This will set:
# 1) `numpy` seed
# 2) backend random seed
# 3) `python` random seed
keras.utils.set_random_seed(812)

# If using TensorFlow, this will make GPU ops as deterministic as possible,
# but it will affect the overall performance, so be mindful of that.
tf.config.experimental.enable_op_determinism()

# to suppress warnings
import warnings
warnings.filterwarnings("ignore")


# Importing the dataset
data = pd.read_csv('/content/used_cars_data.csv')

# Check the top five records of the data
data.head()


# checking shape of the data
print(f"There are {data.shape[0]} rows and {data.shape[1]} columns.")

There are 7252 rows and 14 columns.


# let's view a sample of the data
data.sample(n=10, random_state=1)


# checking column datatypes and number of non-null values
df = data.copy()
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7252 entries, 0 to 7251
Data columns (total 14 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Location           7252 non-null   object 
 1   Year               7252 non-null   int64  
 2   Kilometers_Driven  7251 non-null   float64
 3   Fuel_Type          7252 non-null   object 
 4   Transmission       7252 non-null   object 
 5   Owner_Type         7252 non-null   object 
 6   Seats              7199 non-null   float64
 7   New_Price          7252 non-null   float64
 8   Price              6019 non-null   float64
 9   mileage_num        7169 non-null   float64
 10  engine_num         7206 non-null   float64
 11  power_num          7077 non-null   float64
 12  Brand              7252 non-null   object 
 13  Model              7252 non-null   object 
dtypes: float64(7), int64(1), object(6)
memory usage: 793.3+ KB


# checking for duplicate values
df.duplicated().sum()

2


df[df.duplicated(keep=False) == True]


df.drop(4781, inplace=True)
df.drop(7077, inplace=True)


# checking for duplicate values
df.duplicated().sum()

0


df.isnull().sum()


# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


df1 = df.copy()


histogram_boxplot(df1, "Kilometers_Driven", bins=100, kde=True)


df1["kilometers_driven_log"] = np.log(df1["Kilometers_Driven"])


histogram_boxplot(df1, "kilometers_driven_log", bins=100, kde=True)


histogram_boxplot(df1, "mileage_num", kde=True)


histogram_boxplot(df1, "engine_num", kde=True)


histogram_boxplot(df1, "power_num", kde=True)


# creating histograms
df.hist(figsize=(14, 14))
plt.show()


histogram_boxplot(df1, "New_Price", kde=True)


df1["New_price_log"] = np.log(df1["New_Price"])


histogram_boxplot(df1, "New_price_log", bins=100, kde=True)


labeled_barplot(df1, "Brand", perc=True, n=10)


labeled_barplot(df1, "Location", perc=True)


labeled_barplot(df1, "Fuel_Type", perc=True)


labeled_barplot(df1, "Seats", perc=True)


labeled_barplot(df1, "Transmission", perc=True)


labeled_barplot(df1, "Owner_Type", perc=True)


# If 'Location' needs to be converted to a numeric representation (e.g., using one-hot encoding):
!pip install scikit-learn
import pandas as pd
from sklearn.preprocessing import OneHotEncoder

# Apply one-hot encoding to the categorical columns
df_encoded = pd.get_dummies(df, columns=['Location', 'Fuel_Type', 'Transmission', 'Owner_Type', 'Brand', 'Model'])

# Select the relevant numeric columns, including one-hot encoded features if necessary
numeric_columns = ['Year', 'Kilometers_Driven', 'Seats', 'New_Price', 'Price', 'mileage_num', 'engine_num', 'power_num']

# Create a correlation matrix for the selected numeric columns
correlation_matrix = df_encoded[numeric_columns].corr()

# Plot the correlation matrix as a heatmap
plt.figure(figsize=(15, 7))
sns.heatmap(
    correlation_matrix, annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.show()

Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)
Requirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)
Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)
Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)


plt.figure(figsize=(5, 5))
sns.boxplot(x="Transmission", y="Price", data=df1)
plt.show()


plt.figure(figsize=(18, 5))
sns.boxplot(x="Brand", y="Price", data=df1)
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize=(18, 5))
sns.boxplot(x="Owner_Type", y="Price", data=df1)
plt.show()


sns.pairplot(data=df, hue="Fuel_Type")
plt.show()


plt.figure(figsize=(18, 5))
sns.boxplot(x="Fuel_Type", y="Price", data=df1)
plt.show()


plt.figure(figsize=(12, 5))
sns.boxplot(x="Location", y="Price", data=df1)
plt.show()


plt.figure(figsize=(18, 5))
sns.boxplot(x="Brand", y="Price", data=df)
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize=(18, 5))
sns.boxplot(x="Year", y="Price", data=df1)
plt.show()


# considering only the data points where price is not missing
df = df[df["Price"].notna()].copy()

# checking for missing values
df.isnull().sum()


df.dtypes


data_car = df[['Brand', 'Model']].copy()


df = pd.get_dummies(df,
    columns=df.select_dtypes(include=["object","int64"]).columns.tolist(),
    drop_first=True,dtype=int
)


# Adding Brand and Model which is stored in data_car variable
# These will be needed during missing value imputation
df_final = pd.concat([df,data_car], axis=1)


df_final.shape

(6018, 287)


df_final.head()


# defining the dependent and independent variables
X = df_final.drop(["Price"], axis=1)
y = df_final["Price"]


# splitting the data in 80:20 ratio for train and temporary data
x_train, x_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2,random_state=1)


# splitting the temporary data in 50:50 ratio for validation and test data
x_val,x_test,y_val,y_test = train_test_split(x_temp,y_temp,test_size=0.5,random_state=1)


print("Number of rows in train data =", x_train.shape[0])
print("Number of rows in validation data =", x_val.shape[0])
print("Number of rows in test data =", x_test.shape[0])

Number of rows in train data = 4814
Number of rows in validation data = 602
Number of rows in test data = 602


def print_missing_values_columns(df):
    """
    Filters and prints only the columns from the DataFrame df that contain missing values.

    Parameters:
    - df: DataFrame
        The DataFrame to check for missing values.
    """
    missing_values_columns = df.columns[df.isnull().any()]
    missing_values_counts = df[missing_values_columns].isnull().sum()
    print(missing_values_counts)


# train data
print_missing_values_columns(x_train)

Kilometers_Driven      1
Seats                 39
mileage_num           59
engine_num            34
power_num            116
dtype: int64


# validation data
print_missing_values_columns(x_val)

Seats           1
mileage_num     5
power_num      13
dtype: int64


# test data
print_missing_values_columns(x_test)

Seats           2
mileage_num     6
engine_num      2
power_num      14
dtype: int64


# first, we calculate the median of Seats in the train set grouped by Brand and Model and store in train_grouped_median
train_grouped_median = x_train.groupby(["Brand", "Model"])["Seats"].median()
train_grouped_median


# we will use the calculated median (train_grouped_median) to fill missing values in Seats for corresponding groups in the train set
x_train["Seats"] = x_train.apply(lambda row: row["Seats"] if not pd.isna(row["Seats"]) else train_grouped_median.get((row["Brand"], row["Model"]), np.nan), axis=1)


# checking data points where Seats is still missing
x_train[x_train["Seats"].isnull()]


x_train["Seats"] = x_train["Seats"].fillna(5.0)


# we will use the calculated median (train_grouped_median) to fill missing values in Seats for corresponding groups in the validation set
x_val["Seats"] = x_val.apply(lambda row: row["Seats"] if not pd.isna(row["Seats"]) else train_grouped_median.get((row["Brand"], row["Model"]), np.nan), axis=1)


# checking the missing values in x_val
print_missing_values_columns(x_val)

Seats           1
mileage_num     5
power_num      13
dtype: int64


# checking data points where Seats is still missing
x_val[x_val["Seats"].isnull()]


x_val["Seats"] = x_val["Seats"].fillna(5.0)


# checking the missing values in x_val
print_missing_values_columns(x_val)

mileage_num     5
power_num      13
dtype: int64


# Same method is applied on test data
x_test["Seats"] = x_test.apply(lambda row: row["Seats"] if not pd.isna(row["Seats"]) else train_grouped_median.get((row["Brand"], row["Model"]), np.nan), axis=1)


# checking the missing values in x_test
print_missing_values_columns(x_test)

mileage_num     6
engine_num      2
power_num      14
dtype: int64


cols_list = ["Kilometers_Driven","mileage_num", "engine_num", "power_num"]

# Step 1: Calculate the median of specified columns in x_train grouped by Brand and Model
train_grouped_median = x_train.groupby(["Brand", "Model"])[cols_list].median()

# Step 2: Use the calculated median to fill missing values in specified columns for corresponding groups in train, validation and test data
for col in cols_list:
    x_train[col] = x_train.apply(lambda row: row[col] if not pd.isna(row[col]) else train_grouped_median[col].get((row["Brand"], row["Model"]), np.nan), axis=1)
    x_val[col] = x_val.apply(lambda row: row[col] if not pd.isna(row[col]) else train_grouped_median[col].get((row["Brand"], row["Model"]), np.nan), axis=1)
    x_test[col] = x_test.apply(lambda row: row[col] if not pd.isna(row[col]) else train_grouped_median[col].get((row["Brand"], row["Model"]), np.nan), axis=1)


# checking the missing values in x_train
print_missing_values_columns(x_train)

mileage_num    7
power_num      9
dtype: int64


# checking the missing values in x_val
print_missing_values_columns(x_val)

mileage_num    1
power_num      1
dtype: int64


# checking the missing values in x_test
print_missing_values_columns(x_test)

mileage_num    1
power_num      1
dtype: int64


cols_list = ["mileage_num", "power_num"]

# Step 1: Calculate the median of specified columns in x_train grouped by Brand and Model
train_grouped_median = x_train.groupby(["Brand"])[cols_list].median()

# Step 2: Use the calculated median to fill missing values in specified columns for corresponding groups in train, validation and test data
for col in cols_list:
    x_train[col] = x_train.apply(lambda row: row[col] if not pd.isna(row[col]) else train_grouped_median[col].get((row["Brand"]), np.nan), axis=1)
    x_val[col] = x_val.apply(lambda row: row[col] if not pd.isna(row[col]) else train_grouped_median[col].get((row["Brand"]), np.nan), axis=1)
    x_test[col] = x_test.apply(lambda row: row[col] if not pd.isna(row[col]) else train_grouped_median[col].get((row["Brand"]), np.nan), axis=1)


print_missing_values_columns(x_train)

mileage_num    1
power_num      1
dtype: int64


print_missing_values_columns(x_val)

Series([], dtype: float64)


print_missing_values_columns(x_test)

Series([], dtype: float64)


cols_list = ["mileage_num", "power_num"]

for col in cols_list:
    x_train[col] = x_train[col].fillna(df[col].median())


print_missing_values_columns(x_train)

Series([], dtype: float64)


# Dropping Brand and Model from train, validation, and test data as we already have dummy variables for them
x_train = x_train.drop(['Brand','Model'],axis=1)
x_val = x_val.drop(['Brand','Model'],axis=1)
x_test = x_test.drop(['Brand','Model'],axis=1)


# Define the columns to scale
num_columns = ["Kilometers_Driven", "Seats", "New_Price", "mileage_num", "engine_num", "power_num"]

# Initialize the StandardScaler
scaler = StandardScaler()

# Fit the scaler to the selected columns in the x_train data
scaler.fit(x_train[num_columns])

StandardScaler()

StandardScaler()


# Transform selected columns in x_train, x_val, and x_test using the fitted scaler

x_train[num_columns] = scaler.transform(x_train[num_columns])

x_val[num_columns] = scaler.transform(x_val[num_columns])

x_test[num_columns] = scaler.transform(x_test[num_columns])


x_train.head()


def plot(history, name):
    """
    Function to plot loss/accuracy

    history: an object which stores the metrics and losses.
    name: can be one of Loss or Accuracy
    """
    fig, ax = plt.subplots() #Creating a subplot with figure and axes.
    plt.plot(history.history[name]) #Plotting the train accuracy or train loss
    plt.plot(history.history['val_'+name]) #Plotting the validation accuracy or validation loss

    plt.title('Model ' + name.capitalize()) #Defining the title of the plot.
    plt.ylabel(name.capitalize()) #Capitalizing the first letter.
    plt.xlabel('Epoch') #Defining the label for the x-axis.
    fig.legend(['Train', 'Validation'], loc="outside right upper") #Defining the legend, loc controls the position of the legend.


# function to compute adjusted R-squared
def adj_r2_score(predictors, targets, predictions):
    r2 = r2_score(targets, predictions)
    n = predictors.shape[0]
    k = predictors.shape[1]
    return 1 - ((1 - r2) * (n - 1) / (n - k - 1))


# function to compute MAPE
def mape_score(targets, predictions):
    return np.mean(np.abs(targets - predictions) / targets) * 100


# function to compute different metrics to check performance of a neural network model
def model_performance(model,predictors,target):
    """
    Function to compute different metrics to check regression model performance

    model: regressor
    predictors: independent variables
    target: dependent variable
    """
    # predicting using the independent variables
    pred = model.predict(predictors).reshape(-1)

    r2 = r2_score(target, pred)  # to compute R-squared
    adjr2 = adj_r2_score(predictors, target, pred)  # to compute adjusted R-squared
    rmse = np.sqrt(mean_squared_error(target, pred))  # to compute RMSE
    mae = mean_absolute_error(target, pred)  # to compute MAE
    mape = mape_score(target, pred)  # to compute MAPE

    # creating a dataframe of metrics
    df_perf =  {
            "RMSE": [rmse],
            "MAE": [mae],
            "R-squared": [r2],
            "Adj. R-squared": [adjr2],
            "MAPE": [mape]}

    return df_perf

columns = ["# hidden layers","# neurons - hidden layer","activation function - hidden layer ","# epochs","batch size","optimizer","time(secs)","Train_loss","Valid_loss","Train_R-squared","Valid_R-squared"]

results = pd.DataFrame(columns=columns)


#Defining the list of metrics to be used for all the models.
metrics = [tf.keras.metrics.R2Score(name="r2_score")]


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(1,input_dim=x_train.shape[1]))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 1)                   │             285 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 285 (1.11 KB)

 Trainable params: 285 (1.11 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 10
batch_size = x_train.shape[0]


import time # imports the time module
start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step - loss: 215.6193 - r2_score: -0.7024 - val_loss: 231.8262 - val_r2_score: -0.6387
Epoch 2/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - loss: 199.6886 - r2_score: -0.5766 - val_loss: 215.1222 - val_r2_score: -0.5207
Epoch 3/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 98ms/step - loss: 185.3213 - r2_score: -0.4632 - val_loss: 200.0059 - val_r2_score: -0.4138
Epoch 4/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 96ms/step - loss: 172.3545 - r2_score: -0.3608 - val_loss: 186.3157 - val_r2_score: -0.3170
Epoch 5/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 135ms/step - loss: 160.6429 - r2_score: -0.2683 - val_loss: 173.9070 - val_r2_score: -0.2293
Epoch 6/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - loss: 150.0572 - r2_score: -0.1848 - val_loss: 162.6510 - val_r2_score: -0.1498
Epoch 7/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 134ms/step - loss: 140.4819 - r2_score: -0.1092 - val_loss: 152.4325 - val_r2_score: -0.0775
Epoch 8/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 138ms/step - loss: 131.8141 - r2_score: -0.0407 - val_loss: 143.1481 - val_r2_score: -0.0119
Epoch 9/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 97ms/step - loss: 123.9616 - r2_score: 0.0213 - val_loss: 134.7058 - val_r2_score: 0.0478
Epoch 10/10
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 191ms/step - loss: 116.8424 - r2_score: 0.0775 - val_loss: 127.0229 - val_r2_score: 0.1021


print("Time taken in seconds ",end-start)

Time taken in seconds  1.6971805095672607


plot(history,'loss')


plot(history,'r2_score')


results.loc[0]=['-','-','-',epochs,batch_size,'GD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(1,input_dim=x_train.shape[1]))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 1)                   │             285 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 285 (1.11 KB)

 Trainable params: 285 (1.11 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = x_train.shape[0]


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - loss: 212.9668 - r2_score: -0.5842 - val_loss: 229.4064 - val_r2_score: -0.6216
Epoch 2/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - loss: 197.4542 - r2_score: -0.5590 - val_loss: 213.0991 - val_r2_score: -0.5064
Epoch 3/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 103ms/step - loss: 183.4492 - r2_score: -0.4484 - val_loss: 198.3264 - val_r2_score: -0.4019
Epoch 4/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 124ms/step - loss: 170.7959 - r2_score: -0.3485 - val_loss: 184.9332 - val_r2_score: -0.3073
Epoch 5/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 99ms/step - loss: 159.3555 - r2_score: -0.2582 - val_loss: 172.7811 - val_r2_score: -0.2214
Epoch 6/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 129ms/step - loss: 149.0038 - r2_score: -0.1764 - val_loss: 161.7463 - val_r2_score: -0.1434
Epoch 7/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 152ms/step - loss: 139.6302 - r2_score: -0.1024 - val_loss: 151.7180 - val_r2_score: -0.0725
Epoch 8/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 95ms/step - loss: 131.1358 - r2_score: -0.0354 - val_loss: 142.5970 - val_r2_score: -0.0080
Epoch 9/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - loss: 123.4324 - r2_score: 0.0255 - val_loss: 134.2945 - val_r2_score: 0.0507
Epoch 10/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 93ms/step - loss: 116.4407 - r2_score: 0.0807 - val_loss: 126.7310 - val_r2_score: 0.1042
Epoch 11/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 137ms/step - loss: 110.0903 - r2_score: 0.1308 - val_loss: 119.8349 - val_r2_score: 0.1529
Epoch 12/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 144ms/step - loss: 104.3176 - r2_score: 0.1764 - val_loss: 113.5422 - val_r2_score: 0.1974
Epoch 13/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 104ms/step - loss: 99.0660 - r2_score: 0.2178 - val_loss: 107.7954 - val_r2_score: 0.2380
Epoch 14/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 131ms/step - loss: 94.2847 - r2_score: 0.2556 - val_loss: 102.5428 - val_r2_score: 0.2751
Epoch 15/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 95ms/step - loss: 89.9281 - r2_score: 0.2900 - val_loss: 97.7379 - val_r2_score: 0.3091
Epoch 16/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - loss: 85.9551 - r2_score: 0.3214 - val_loss: 93.3388 - val_r2_score: 0.3402
Epoch 17/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 244ms/step - loss: 82.3291 - r2_score: 0.3500 - val_loss: 89.3079 - val_r2_score: 0.3687
Epoch 18/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 138ms/step - loss: 79.0171 - r2_score: 0.3761 - val_loss: 85.6112 - val_r2_score: 0.3948
Epoch 19/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 169ms/step - loss: 75.9892 - r2_score: 0.4000 - val_loss: 82.2183 - val_r2_score: 0.4188
Epoch 20/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - loss: 73.2189 - r2_score: 0.4219 - val_loss: 79.1014 - val_r2_score: 0.4408
Epoch 21/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 145ms/step - loss: 70.6820 - r2_score: 0.4419 - val_loss: 76.2357 - val_r2_score: 0.4611
Epoch 22/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 106ms/step - loss: 68.3569 - r2_score: 0.4603 - val_loss: 73.5987 - val_r2_score: 0.4797
Epoch 23/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 96ms/step - loss: 66.2241 - r2_score: 0.4771 - val_loss: 71.1701 - val_r2_score: 0.4969
Epoch 24/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 91ms/step - loss: 64.2660 - r2_score: 0.4926 - val_loss: 68.9315 - val_r2_score: 0.5127
Epoch 25/25
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 141ms/step - loss: 62.4666 - r2_score: 0.5068 - val_loss: 66.8663 - val_r2_score: 0.5273


print("Time taken in seconds ",end-start)

Time taken in seconds  3.8399524688720703


plot(history,'loss')


plot(history,'r2_score')


results.loc[1]=['-','-','-',epochs,batch_size,'GD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(1,input_dim=x_train.shape[1]))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 1)                   │             285 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 285 (1.11 KB)

 Trainable params: 285 (1.11 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 32


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 86.8465 - r2_score: 0.4432 - val_loss: 35.5213 - val_r2_score: 0.7489
Epoch 2/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 23ms/step - loss: 33.6671 - r2_score: 0.7365 - val_loss: 32.9409 - val_r2_score: 0.7671
Epoch 3/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 3s 23ms/step - loss: 31.4131 - r2_score: 0.7541 - val_loss: 31.3458 - val_r2_score: 0.7784
Epoch 4/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 6s 30ms/step - loss: 30.0467 - r2_score: 0.7648 - val_loss: 30.2675 - val_r2_score: 0.7860
Epoch 5/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 22ms/step - loss: 29.1110 - r2_score: 0.7721 - val_loss: 29.4811 - val_r2_score: 0.7916
Epoch 6/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 5s 23ms/step - loss: 28.4153 - r2_score: 0.7775 - val_loss: 28.8686 - val_r2_score: 0.7959
Epoch 7/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 5s 30ms/step - loss: 27.8644 - r2_score: 0.7818 - val_loss: 28.3658 - val_r2_score: 0.7995
Epoch 8/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 5s 27ms/step - loss: 27.4077 - r2_score: 0.7854 - val_loss: 27.9365 - val_r2_score: 0.8025
Epoch 9/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 23ms/step - loss: 27.0161 - r2_score: 0.7884 - val_loss: 27.5594 - val_r2_score: 0.8052
Epoch 10/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 30ms/step - loss: 26.6721 - r2_score: 0.7911 - val_loss: 27.2215 - val_r2_score: 0.8076
Epoch 11/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 23ms/step - loss: 26.3648 - r2_score: 0.7935 - val_loss: 26.9147 - val_r2_score: 0.8097
Epoch 12/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 6s 32ms/step - loss: 26.0867 - r2_score: 0.7957 - val_loss: 26.6333 - val_r2_score: 0.8117
Epoch 13/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 5s 28ms/step - loss: 25.8324 - r2_score: 0.7976 - val_loss: 26.3735 - val_r2_score: 0.8136
Epoch 14/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 23ms/step - loss: 25.5982 - r2_score: 0.7995 - val_loss: 26.1323 - val_r2_score: 0.8153
Epoch 15/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 8s 39ms/step - loss: 25.3811 - r2_score: 0.8012 - val_loss: 25.9075 - val_r2_score: 0.8169
Epoch 16/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 3s 23ms/step - loss: 25.1790 - r2_score: 0.8027 - val_loss: 25.6974 - val_r2_score: 0.8183
Epoch 17/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 5s 23ms/step - loss: 24.9900 - r2_score: 0.8042 - val_loss: 25.5004 - val_r2_score: 0.8197
Epoch 18/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 6s 28ms/step - loss: 24.8125 - r2_score: 0.8056 - val_loss: 25.3153 - val_r2_score: 0.8210
Epoch 19/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 3s 23ms/step - loss: 24.6455 - r2_score: 0.8069 - val_loss: 25.1410 - val_r2_score: 0.8223
Epoch 20/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 6s 26ms/step - loss: 24.4877 - r2_score: 0.8081 - val_loss: 24.9766 - val_r2_score: 0.8234
Epoch 21/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 24.3385 - r2_score: 0.8093 - val_loss: 24.8212 - val_r2_score: 0.8245
Epoch 22/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 23ms/step - loss: 24.1969 - r2_score: 0.8104 - val_loss: 24.6741 - val_r2_score: 0.8256
Epoch 23/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 6s 27ms/step - loss: 24.0624 - r2_score: 0.8114 - val_loss: 24.5347 - val_r2_score: 0.8266
Epoch 24/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 4s 23ms/step - loss: 23.9343 - r2_score: 0.8124 - val_loss: 24.4023 - val_r2_score: 0.8275
Epoch 25/25
151/151 ━━━━━━━━━━━━━━━━━━━━ 5s 23ms/step - loss: 23.8121 - r2_score: 0.8134 - val_loss: 24.2764 - val_r2_score: 0.8284


print("Time taken in seconds ",end-start)

Time taken in seconds  121.4993999004364


plot(history,'loss')


plot(history,'r2_score')


results.loc[2]=['-','-','-',epochs,batch_size,'SGD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(1,input_dim=x_train.shape[1]))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 1)                   │             285 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 285 (1.11 KB)

 Trainable params: 285 (1.11 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 64


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - loss: 111.9825 - r2_score: 0.3936 - val_loss: 38.6424 - val_r2_score: 0.7268
Epoch 2/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 25ms/step - loss: 36.7211 - r2_score: 0.7141 - val_loss: 35.5817 - val_r2_score: 0.7485
Epoch 3/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - loss: 34.1328 - r2_score: 0.7338 - val_loss: 34.1166 - val_r2_score: 0.7588
Epoch 4/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - loss: 32.7173 - r2_score: 0.7448 - val_loss: 33.0035 - val_r2_score: 0.7667
Epoch 5/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - loss: 31.6776 - r2_score: 0.7529 - val_loss: 32.1211 - val_r2_score: 0.7729
Epoch 6/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 30.8699 - r2_score: 0.7592 - val_loss: 31.4058 - val_r2_score: 0.7780
Epoch 7/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 30.2217 - r2_score: 0.7642 - val_loss: 30.8142 - val_r2_score: 0.7822
Epoch 8/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - loss: 29.6879 - r2_score: 0.7684 - val_loss: 30.3152 - val_r2_score: 0.7857
Epoch 9/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 23ms/step - loss: 29.2380 - r2_score: 0.7718 - val_loss: 29.8866 - val_r2_score: 0.7887
Epoch 10/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 23ms/step - loss: 28.8512 - r2_score: 0.7748 - val_loss: 29.5122 - val_r2_score: 0.7914
Epoch 11/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - loss: 28.5128 - r2_score: 0.7775 - val_loss: 29.1803 - val_r2_score: 0.7937
Epoch 12/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 28.2123 - r2_score: 0.7798 - val_loss: 28.8820 - val_r2_score: 0.7958
Epoch 13/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - loss: 27.9418 - r2_score: 0.7819 - val_loss: 28.6108 - val_r2_score: 0.7978
Epoch 14/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 25ms/step - loss: 27.6958 - r2_score: 0.7838 - val_loss: 28.3617 - val_r2_score: 0.7995
Epoch 15/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 32ms/step - loss: 27.4698 - r2_score: 0.7856 - val_loss: 28.1310 - val_r2_score: 0.8011
Epoch 16/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - loss: 27.2606 - r2_score: 0.7872 - val_loss: 27.9158 - val_r2_score: 0.8027
Epoch 17/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step - loss: 27.0657 - r2_score: 0.7887 - val_loss: 27.7139 - val_r2_score: 0.8041
Epoch 18/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 26.8830 - r2_score: 0.7901 - val_loss: 27.5234 - val_r2_score: 0.8054
Epoch 19/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - loss: 26.7110 - r2_score: 0.7915 - val_loss: 27.3431 - val_r2_score: 0.8067
Epoch 20/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 24ms/step - loss: 26.5485 - r2_score: 0.7927 - val_loss: 27.1717 - val_r2_score: 0.8079
Epoch 21/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - loss: 26.3942 - r2_score: 0.7939 - val_loss: 27.0083 - val_r2_score: 0.8091
Epoch 22/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - loss: 26.2475 - r2_score: 0.7951 - val_loss: 26.8523 - val_r2_score: 0.8102
Epoch 23/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 26.1075 - r2_score: 0.7961 - val_loss: 26.7029 - val_r2_score: 0.8112
Epoch 24/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 25.9737 - r2_score: 0.7972 - val_loss: 26.5596 - val_r2_score: 0.8123
Epoch 25/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - loss: 25.8455 - r2_score: 0.7982 - val_loss: 26.4221 - val_r2_score: 0.8132


print("Time taken in seconds ",end-start)

Time taken in seconds  57.31793189048767


plot(history,'loss')


plot(history,'r2_score')


results.loc[3]=['-','-','-',epochs,batch_size,'SGD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(128,activation="sigmoid",input_dim=x_train.shape[1]))
model.add(Dense(1))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 128)                 │          36,480 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 1)                   │             129 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 36,609 (143.00 KB)

 Trainable params: 36,609 (143.00 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 64


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 100.2605 - r2_score: 0.4408 - val_loss: 36.9329 - val_r2_score: 0.7389
Epoch 2/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 36.0253 - r2_score: 0.7199 - val_loss: 33.1919 - val_r2_score: 0.7654
Epoch 3/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 34ms/step - loss: 31.8483 - r2_score: 0.7519 - val_loss: 30.7305 - val_r2_score: 0.7828
Epoch 4/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 30ms/step - loss: 29.2322 - r2_score: 0.7721 - val_loss: 28.7463 - val_r2_score: 0.7968
Epoch 5/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 27.2142 - r2_score: 0.7876 - val_loss: 27.1094 - val_r2_score: 0.8084
Epoch 6/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 25.5121 - r2_score: 0.8008 - val_loss: 25.6944 - val_r2_score: 0.8184
Epoch 7/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 23.9985 - r2_score: 0.8125 - val_loss: 24.4229 - val_r2_score: 0.8274
Epoch 8/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 28ms/step - loss: 22.6255 - r2_score: 0.8231 - val_loss: 23.2575 - val_r2_score: 0.8356
Epoch 9/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 21.3786 - r2_score: 0.8328 - val_loss: 22.1796 - val_r2_score: 0.8432
Epoch 10/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 27ms/step - loss: 20.2531 - r2_score: 0.8415 - val_loss: 21.1771 - val_r2_score: 0.8503
Epoch 11/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 19.2443 - r2_score: 0.8494 - val_loss: 20.2422 - val_r2_score: 0.8569
Epoch 12/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 18.3453 - r2_score: 0.8563 - val_loss: 19.3709 - val_r2_score: 0.8631
Epoch 13/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 29ms/step - loss: 17.5467 - r2_score: 0.8626 - val_loss: 18.5624 - val_r2_score: 0.8688
Epoch 14/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 29ms/step - loss: 16.8377 - r2_score: 0.8681 - val_loss: 17.8170 - val_r2_score: 0.8741
Epoch 15/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 16.2078 - r2_score: 0.8730 - val_loss: 17.1348 - val_r2_score: 0.8789
Epoch 16/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - loss: 15.6470 - r2_score: 0.8774 - val_loss: 16.5148 - val_r2_score: 0.8833
Epoch 17/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 15.1466 - r2_score: 0.8813 - val_loss: 15.9552 - val_r2_score: 0.8872
Epoch 18/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 14.6988 - r2_score: 0.8848 - val_loss: 15.4533 - val_r2_score: 0.8908
Epoch 19/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 14.2969 - r2_score: 0.8879 - val_loss: 15.0056 - val_r2_score: 0.8939
Epoch 20/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 31ms/step - loss: 13.9348 - r2_score: 0.8908 - val_loss: 14.6080 - val_r2_score: 0.8967
Epoch 21/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 38ms/step - loss: 13.6071 - r2_score: 0.8934 - val_loss: 14.2554 - val_r2_score: 0.8992
Epoch 22/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 29ms/step - loss: 13.3093 - r2_score: 0.8957 - val_loss: 13.9424 - val_r2_score: 0.9014
Epoch 23/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 13.0374 - r2_score: 0.8979 - val_loss: 13.6638 - val_r2_score: 0.9034
Epoch 24/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 12.7880 - r2_score: 0.8998 - val_loss: 13.4147 - val_r2_score: 0.9052
Epoch 25/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 39ms/step - loss: 12.5583 - r2_score: 0.9016 - val_loss: 13.1910 - val_r2_score: 0.9068


print("Time taken in seconds ",end-start)

Time taken in seconds  74.1546471118927


plot(history,'loss')


plot(history,'r2_score')


results.loc[4]=[1,128,'sigmoid',epochs,batch_size,'SGD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(128,activation="tanh",input_dim=x_train.shape[1]))
model.add(Dense(1))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 128)                 │          36,480 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 1)                   │             129 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 36,609 (143.00 KB)

 Trainable params: 36,609 (143.00 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 64


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - loss: 79.5473 - r2_score: 0.5943 - val_loss: 35.1616 - val_r2_score: 0.7514
Epoch 2/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 30.2468 - r2_score: 0.7651 - val_loss: 27.9162 - val_r2_score: 0.8027
Epoch 3/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 36ms/step - loss: 25.7403 - r2_score: 0.7997 - val_loss: 23.4933 - val_r2_score: 0.8339
Epoch 4/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 22.1494 - r2_score: 0.8272 - val_loss: 21.4471 - val_r2_score: 0.8484
Epoch 5/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 19.6231 - r2_score: 0.8466 - val_loss: 19.7989 - val_r2_score: 0.8600
Epoch 6/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 17.8089 - r2_score: 0.8607 - val_loss: 18.0529 - val_r2_score: 0.8724
Epoch 7/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 31ms/step - loss: 16.4196 - r2_score: 0.8715 - val_loss: 16.4278 - val_r2_score: 0.8839
Epoch 8/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 38ms/step - loss: 15.3019 - r2_score: 0.8802 - val_loss: 15.0046 - val_r2_score: 0.8939
Epoch 9/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 14.3681 - r2_score: 0.8874 - val_loss: 13.8965 - val_r2_score: 0.9018
Epoch 10/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 13.5662 - r2_score: 0.8936 - val_loss: 13.0059 - val_r2_score: 0.9081
Epoch 11/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 12.8818 - r2_score: 0.8989 - val_loss: 12.3403 - val_r2_score: 0.9128
Epoch 12/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 12.2779 - r2_score: 0.9036 - val_loss: 11.8500 - val_r2_score: 0.9162
Epoch 13/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 41ms/step - loss: 11.7520 - r2_score: 0.9077 - val_loss: 11.4965 - val_r2_score: 0.9187
Epoch 14/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 11.2884 - r2_score: 0.9113 - val_loss: 11.2293 - val_r2_score: 0.9206
Epoch 15/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 27ms/step - loss: 10.8749 - r2_score: 0.9146 - val_loss: 11.0060 - val_r2_score: 0.9222
Epoch 16/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 10.5038 - r2_score: 0.9175 - val_loss: 10.8015 - val_r2_score: 0.9236
Epoch 17/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 10.1706 - r2_score: 0.9201 - val_loss: 10.6096 - val_r2_score: 0.9250
Epoch 18/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 28ms/step - loss: 9.8682 - r2_score: 0.9225 - val_loss: 10.4366 - val_r2_score: 0.9262
Epoch 19/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 9.5903 - r2_score: 0.9247 - val_loss: 10.2879 - val_r2_score: 0.9273
Epoch 20/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 9.3318 - r2_score: 0.9267 - val_loss: 10.1541 - val_r2_score: 0.9282
Epoch 21/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 37ms/step - loss: 9.0828 - r2_score: 0.9287 - val_loss: 10.0277 - val_r2_score: 0.9291
Epoch 22/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 8.8366 - r2_score: 0.9306 - val_loss: 9.9145 - val_r2_score: 0.9299
Epoch 23/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 8.5971 - r2_score: 0.9325 - val_loss: 9.8223 - val_r2_score: 0.9306
Epoch 24/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 8.3698 - r2_score: 0.9343 - val_loss: 9.7551 - val_r2_score: 0.9310
Epoch 25/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 8.1562 - r2_score: 0.9360 - val_loss: 9.7151 - val_r2_score: 0.9313


print("Time taken in seconds ",end-start)

Time taken in seconds  78.1450309753418


plot(history,'loss')


plot(history,'r2_score')


results.loc[5]=[1,128,'tanh',epochs,batch_size,'SGD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(128,activation="relu",input_dim=x_train.shape[1]))
model.add(Dense(1))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 128)                 │          36,480 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 1)                   │             129 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 36,609 (143.00 KB)

 Trainable params: 36,609 (143.00 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 64


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step - loss: 78.1409 - r2_score: 0.6149 - val_loss: 22.6298 - val_r2_score: 0.8400
Epoch 2/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 24.4104 - r2_score: 0.8088 - val_loss: 20.3330 - val_r2_score: 0.8563
Epoch 3/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 20.8962 - r2_score: 0.8361 - val_loss: 20.2712 - val_r2_score: 0.8567
Epoch 4/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - loss: 18.9292 - r2_score: 0.8516 - val_loss: 19.0919 - val_r2_score: 0.8650
Epoch 5/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 29ms/step - loss: 17.5839 - r2_score: 0.8622 - val_loss: 18.7435 - val_r2_score: 0.8675
Epoch 6/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - loss: 16.6700 - r2_score: 0.8696 - val_loss: 18.1998 - val_r2_score: 0.8713
Epoch 7/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 15.9100 - r2_score: 0.8757 - val_loss: 18.0499 - val_r2_score: 0.8724
Epoch 8/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 36ms/step - loss: 15.2037 - r2_score: 0.8814 - val_loss: 17.2522 - val_r2_score: 0.8780
Epoch 9/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - loss: 14.6969 - r2_score: 0.8855 - val_loss: 16.7128 - val_r2_score: 0.8819
Epoch 10/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 27ms/step - loss: 14.1779 - r2_score: 0.8896 - val_loss: 16.0255 - val_r2_score: 0.8867
Epoch 11/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 13.7966 - r2_score: 0.8926 - val_loss: 16.2979 - val_r2_score: 0.8848
Epoch 12/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 36ms/step - loss: 13.3378 - r2_score: 0.8964 - val_loss: 15.2482 - val_r2_score: 0.8922
Epoch 13/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 13.0258 - r2_score: 0.8988 - val_loss: 15.3595 - val_r2_score: 0.8914
Epoch 14/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 30ms/step - loss: 12.6184 - r2_score: 0.9021 - val_loss: 14.2531 - val_r2_score: 0.8992
Epoch 15/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 12.3351 - r2_score: 0.9042 - val_loss: 14.5103 - val_r2_score: 0.8974
Epoch 16/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 34ms/step - loss: 11.9488 - r2_score: 0.9074 - val_loss: 14.1340 - val_r2_score: 0.9001
Epoch 17/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 30ms/step - loss: 11.7498 - r2_score: 0.9089 - val_loss: 15.1942 - val_r2_score: 0.8926
Epoch 18/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 11.6430 - r2_score: 0.9100 - val_loss: 14.6469 - val_r2_score: 0.8965
Epoch 19/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 11.2995 - r2_score: 0.9126 - val_loss: 14.3911 - val_r2_score: 0.8983
Epoch 20/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step - loss: 10.9995 - r2_score: 0.9149 - val_loss: 15.4576 - val_r2_score: 0.8907
Epoch 21/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 37ms/step - loss: 11.0765 - r2_score: 0.9145 - val_loss: 13.6486 - val_r2_score: 0.9035
Epoch 22/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 10.4216 - r2_score: 0.9194 - val_loss: 14.7097 - val_r2_score: 0.8960
Epoch 23/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 27ms/step - loss: 10.3984 - r2_score: 0.9196 - val_loss: 14.1583 - val_r2_score: 0.8999
Epoch 24/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 10.1541 - r2_score: 0.9215 - val_loss: 14.2825 - val_r2_score: 0.8990
Epoch 25/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - loss: 9.9332 - r2_score: 0.9232 - val_loss: 14.3455 - val_r2_score: 0.8986


print("Time taken in seconds ",end-start)

Time taken in seconds  78.64779210090637


plot(history,'loss')


plot(history,'r2_score')


results.loc[6]=[1,128,'relu',epochs,batch_size,'SGD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(128,activation="relu",input_dim=x_train.shape[1]))
model.add(Dense(32,activation="relu"))
model.add(Dense(1))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 128)                 │          36,480 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 32)                  │           4,128 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 1)                   │              33 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 40,641 (158.75 KB)

 Trainable params: 40,641 (158.75 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 64


start = time.time()
history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)
end=time.time()

Epoch 1/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 36ms/step - loss: 119.0312 - r2_score: 0.3395 - val_loss: 174.7490 - val_r2_score: -0.2353
Epoch 2/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 141.2652 - r2_score: -0.0883 - val_loss: 125.1895 - val_r2_score: 0.1151
Epoch 3/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 36ms/step - loss: 103.3704 - r2_score: 0.2047 - val_loss: 92.6942 - val_r2_score: 0.3448
Epoch 4/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 33ms/step - loss: 80.2653 - r2_score: 0.3824 - val_loss: 38.2387 - val_r2_score: 0.7297
Epoch 5/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step - loss: 51.9535 - r2_score: 0.5936 - val_loss: 71.4642 - val_r2_score: 0.4948
Epoch 6/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 43ms/step - loss: 74.8095 - r2_score: 0.4208 - val_loss: 64.5067 - val_r2_score: 0.5440
Epoch 7/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 37ms/step - loss: 72.0067 - r2_score: 0.4423 - val_loss: 49.9014 - val_r2_score: 0.6473
Epoch 8/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - loss: 58.6729 - r2_score: 0.5446 - val_loss: 59.2428 - val_r2_score: 0.5812
Epoch 9/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 6s 42ms/step - loss: 62.5613 - r2_score: 0.5163 - val_loss: 57.4270 - val_r2_score: 0.5941
Epoch 10/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 36ms/step - loss: 63.6383 - r2_score: 0.5072 - val_loss: 57.5613 - val_r2_score: 0.5931
Epoch 11/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 57.4395 - r2_score: 0.5558 - val_loss: 47.6287 - val_r2_score: 0.6633
Epoch 12/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 48.6959 - r2_score: 0.6236 - val_loss: 41.1300 - val_r2_score: 0.7093
Epoch 13/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step - loss: 42.1436 - r2_score: 0.6749 - val_loss: 37.0572 - val_r2_score: 0.7380
Epoch 14/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 6s 76ms/step - loss: 39.6427 - r2_score: 0.6938 - val_loss: 33.9403 - val_r2_score: 0.7601
Epoch 15/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 8s 103ms/step - loss: 36.8757 - r2_score: 0.7150 - val_loss: 31.1051 - val_r2_score: 0.7801
Epoch 16/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 33ms/step - loss: 34.6752 - r2_score: 0.7318 - val_loss: 29.2155 - val_r2_score: 0.7935
Epoch 17/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step - loss: 32.7577 - r2_score: 0.7465 - val_loss: 27.1382 - val_r2_score: 0.8082
Epoch 18/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 44ms/step - loss: 31.6345 - r2_score: 0.7547 - val_loss: 25.5313 - val_r2_score: 0.8195
Epoch 19/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 34ms/step - loss: 30.8265 - r2_score: 0.7608 - val_loss: 27.5814 - val_r2_score: 0.8050
Epoch 20/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - loss: 27.6467 - r2_score: 0.7862 - val_loss: 24.0735 - val_r2_score: 0.8298
Epoch 21/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - loss: 29.5538 - r2_score: 0.7706 - val_loss: 23.3580 - val_r2_score: 0.8349
Epoch 22/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 34ms/step - loss: 26.2073 - r2_score: 0.7966 - val_loss: 21.6184 - val_r2_score: 0.8472
Epoch 23/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 34ms/step - loss: 24.5601 - r2_score: 0.8095 - val_loss: 21.9547 - val_r2_score: 0.8448
Epoch 24/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 6s 42ms/step - loss: 24.8704 - r2_score: 0.8068 - val_loss: 22.1077 - val_r2_score: 0.8437
Epoch 25/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 37ms/step - loss: 23.6243 - r2_score: 0.8167 - val_loss: 19.5699 - val_r2_score: 0.8617


print("Time taken in seconds ",end-start)

Time taken in seconds  99.04969620704651


plot(history,'loss')


plot(history,'r2_score')


results.loc[7]=[2,[128,32],['relu','relu'],epochs,batch_size,'SGD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]


results


results


# clears the current Keras session, resetting all layers and models previously created, freeing up memory and resources.
tf.keras.backend.clear_session()


#Initializing the neural network
model = Sequential()
model.add(Dense(128,activation="relu",input_dim=x_train.shape[1]))
model.add(Dense(1))


model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 128)                 │          36,480 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 1)                   │             129 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 36,609 (143.00 KB)

 Trainable params: 36,609 (143.00 KB)

 Non-trainable params: 0 (0.00 B)


optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used
model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)


epochs = 25
batch_size = 64


history = model.fit(x_train, y_train, validation_data=(x_test,y_test) , batch_size=batch_size, epochs=epochs)

Epoch 1/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - loss: 78.9293 - r2_score: 0.5921 - val_loss: 15.2803 - val_r2_score: 0.8406
Epoch 2/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 24.1295 - r2_score: 0.8110 - val_loss: 13.2398 - val_r2_score: 0.8619
Epoch 3/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 33ms/step - loss: 20.7745 - r2_score: 0.8370 - val_loss: 12.4986 - val_r2_score: 0.8696
Epoch 4/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 28ms/step - loss: 18.8243 - r2_score: 0.8524 - val_loss: 12.0415 - val_r2_score: 0.8744
Epoch 5/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 17.5857 - r2_score: 0.8623 - val_loss: 11.4448 - val_r2_score: 0.8806
Epoch 6/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - loss: 16.5880 - r2_score: 0.8702 - val_loss: 11.1857 - val_r2_score: 0.8833
Epoch 7/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 31ms/step - loss: 15.7657 - r2_score: 0.8769 - val_loss: 10.9638 - val_r2_score: 0.8856
Epoch 8/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - loss: 15.0834 - r2_score: 0.8824 - val_loss: 10.9710 - val_r2_score: 0.8855
Epoch 9/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 14.5815 - r2_score: 0.8865 - val_loss: 10.3246 - val_r2_score: 0.8923
Epoch 10/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 28ms/step - loss: 14.1321 - r2_score: 0.8901 - val_loss: 10.7137 - val_r2_score: 0.8882
Epoch 11/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 30ms/step - loss: 13.6907 - r2_score: 0.8937 - val_loss: 10.4253 - val_r2_score: 0.8912
Epoch 12/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 37ms/step - loss: 13.3379 - r2_score: 0.8965 - val_loss: 9.9992 - val_r2_score: 0.8957
Epoch 13/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 28ms/step - loss: 12.9711 - r2_score: 0.8994 - val_loss: 9.8395 - val_r2_score: 0.8973
Epoch 14/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - loss: 12.6858 - r2_score: 0.9017 - val_loss: 9.5834 - val_r2_score: 0.9000
Epoch 15/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step - loss: 12.3908 - r2_score: 0.9040 - val_loss: 9.1654 - val_r2_score: 0.9044
Epoch 16/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 11.9403 - r2_score: 0.9074 - val_loss: 8.9765 - val_r2_score: 0.9063
Epoch 17/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 40ms/step - loss: 11.7436 - r2_score: 0.9090 - val_loss: 8.9314 - val_r2_score: 0.9068
Epoch 18/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 4s 27ms/step - loss: 11.4443 - r2_score: 0.9113 - val_loss: 8.7917 - val_r2_score: 0.9083
Epoch 19/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 35ms/step - loss: 11.2153 - r2_score: 0.9132 - val_loss: 8.5586 - val_r2_score: 0.9107
Epoch 20/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 28ms/step - loss: 10.9767 - r2_score: 0.9150 - val_loss: 8.3914 - val_r2_score: 0.9124
Epoch 21/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 29ms/step - loss: 10.6837 - r2_score: 0.9173 - val_loss: 7.9760 - val_r2_score: 0.9168
Epoch 22/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step - loss: 10.5235 - r2_score: 0.9184 - val_loss: 8.4672 - val_r2_score: 0.9116
Epoch 23/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 34ms/step - loss: 10.2252 - r2_score: 0.9209 - val_loss: 8.0389 - val_r2_score: 0.9161
Epoch 24/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 5s 30ms/step - loss: 10.1904 - r2_score: 0.9211 - val_loss: 7.7792 - val_r2_score: 0.9188
Epoch 25/25
76/76 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step - loss: 9.8304 - r2_score: 0.9238 - val_loss: 7.7395 - val_r2_score: 0.9192


train_perf = model_performance(model,x_train,y_train)
print("Train performance")
pd.DataFrame(train_perf)

151/151 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step
Train performance


x_val.isnull().sum()


y_val.isnull().sum()

0


valid_perf = model_performance(model,x_val,y_val)
print("Validation data performance")
pd.DataFrame(valid_perf)

19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step
Validation data performance


test_perf = model_performance(model,x_test,y_test)
print("Test performance")
pd.DataFrame(test_perf)

19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step
Test performance


print('Author: Oneil Carter')

Author: Oneil Carter


!jupyter nbconvert --to html "/content/Predict_Price_Of_USed_Cars_With_Neural_Networks.ipynb"

[NbConvertApp] WARNING | pattern '/content/Predict_Price_Of_USed_Cars_With_Neural_Networks.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
        to various other formats.

        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--show-config
    Show the application's configuration (human-readable format)
    Equivalent to: [--Application.show_config=True]
--show-config-json
    Show the application's configuration (json format)
    Equivalent to: [--Application.show_config_json=True]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
    Run nbconvert in place, overwriting the existing notebook (only
            relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place,
            overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document.
            This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
    Disable chromium security sandbox when converting to PDF..
    Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
    Shows code input. This flag is only useful for dejavu users.
    Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
    Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
    Whether the HTML in Markdown cells and cell outputs should be sanitized..
    Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
    Set the log level by value or name.
    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
    Default: 30
    Equivalent to: [--Application.log_level]
--config=<Unicode>
    Full path of a config file.
    Default: ''
    Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
    The export format to be used, either one of the built-in formats
            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']
            or a dotted object name that represents the import path for an
            ``Exporter`` class
    Default: ''
    Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
    Name of the template to use
    Default: ''
    Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
    Name of the template file to use
    Default: None
    Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
    as prebuilt extension for the lab template)
    Default: 'light'
    Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
    Whether the HTML in Markdown cells and cell outputs should be sanitized.This
    should be set to True by nbviewer or similar tools.
    Default: False
    Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
    Writer class used to write the
                                        results of the conversion
    Default: 'FilesWriter'
    Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
    PostProcessor class used to write the
                                        results of the conversion
    Default: ''
    Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
    overwrite base name use for output files.
                can only be used when converting one notebook at a time.
    Default: ''
    Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
    Directory to write output(s) to. Defaults
                                  to output to the directory of each notebook. To recover
                                  previous default behaviour (outputting to the current
                                  working directory) use . as the flag value.
    Default: ''
    Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
    The URL prefix for reveal.js (version 3.x).
            This defaults to the reveal CDN, but can be any url pointing to a copy
            of reveal.js.
            For speaker notes to work, this must be a relative path to a local
            copy of reveal.js: e.g., "reveal.js".
            If a relative path is given, it must be a subdirectory of the
            current directory (from which the server is run).
            See the usage documentation
            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
            for more details.
    Default: ''
    Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
    The nbformat version to write.
            Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
    Equivalent to: [--NotebookExporter.nbformat_version]

Examples
--------

    The simplest way to use nbconvert is

            > jupyter nbconvert mynotebook.ipynb --to html

            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].

            > jupyter nbconvert --to latex mynotebook.ipynb

            Both HTML and LaTeX support multiple output templates. LaTeX includes
            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and
            'classic'. You can specify the flavor of the format used.

            > jupyter nbconvert --to html --template lab mynotebook.ipynb

            You can also pipe the output to stdout, rather than a file

            > jupyter nbconvert mynotebook.ipynb --stdout

            PDF is generated via latex

            > jupyter nbconvert mynotebook.ipynb --to pdf

            You can get (and serve) a Reveal.js-powered slideshow

            > jupyter nbconvert myslides.ipynb --to slides --post serve

            Multiple notebooks can be given at the command line in a couple of
            different ways:

            > jupyter nbconvert notebook*.ipynb
            > jupyter nbconvert notebook1.ipynb notebook2.ipynb

            or you can specify the notebooks list in a config file, containing::

                c.NbConvertApp.notebooks = ["my_notebook.ipynb"]

            > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.

	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Seats	New_Price	Price	mileage_num	engine_num	power_num	Brand	Model
0	Mumbai	2010	72000.0	CNG	Manual	First	5.0	5.51	1.75	26.60	998.0	58.16	maruti	wagon
1	Pune	2015	41000.0	Diesel	Manual	First	5.0	16.06	12.50	19.67	1582.0	126.20	hyundai	creta
2	Chennai	2011	46000.0	Petrol	Manual	First	5.0	8.61	4.50	18.20	1199.0	88.70	honda	jazz
3	Chennai	2012	87000.0	Diesel	Manual	First	7.0	11.27	6.00	20.77	1248.0	88.76	maruti	ertiga
4	Coimbatore	2013	40670.0	Diesel	Automatic	Second	5.0	53.14	17.74	15.20	1968.0	140.80	audi	a4

	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Seats	New_Price	Price	mileage_num	engine_num	power_num	Brand	Model
2397	Kolkata	2016	21460.0	Petrol	Manual	First	5.0	9.470	6.00	17.00	1497.0	121.36	ford	ecosport
6218	Kolkata	2013	48000.0	Diesel	Manual	First	5.0	7.880	NaN	23.40	1248.0	74.00	maruti	swift
6737	Mumbai	2015	59500.0	Petrol	Manual	First	7.0	13.580	NaN	17.30	1497.0	117.30	honda	mobilio
3659	Delhi	2015	27000.0	Petrol	Automatic	First	5.0	9.600	5.95	19.00	1199.0	88.70	honda	jazz
4513	Bangalore	2015	19000.0	Diesel	Automatic	Second	5.0	69.675	38.00	16.36	2179.0	187.70	jaguar	xf
599	Coimbatore	2019	40674.0	Diesel	Automatic	First	7.0	28.050	24.82	11.36	2755.0	171.50	toyota	innova
186	Bangalore	2014	37382.0	Diesel	Automatic	First	5.0	86.970	32.00	13.00	2143.0	201.10	mercedes-benz	e-class
305	Kochi	2014	61726.0	Diesel	Automatic	First	5.0	67.100	20.77	17.68	1968.0	174.33	audi	a6
4581	Hyderabad	2013	105000.0	Diesel	Automatic	First	5.0	44.800	19.00	17.32	1968.0	150.00	audi	q3
6616	Delhi	2014	55000.0	Diesel	Automatic	First	5.0	49.490	NaN	11.78	2143.0	167.62	mercedes-benz	new

	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Seats	New_Price	Price	mileage_num	engine_num	power_num	Brand	Model
3623	Hyderabad	2007	52195.0	Petrol	Manual	First	5.0	4.36	1.75	19.7	796.0	46.3	maruti	alto
4781	Hyderabad	2007	52195.0	Petrol	Manual	First	5.0	4.36	1.75	19.7	796.0	46.3	maruti	alto
6940	Kolkata	2017	13000.0	Diesel	Manual	First	5.0	13.58	NaN	26.0	1498.0	98.6	honda	city
7077	Kolkata	2017	13000.0	Diesel	Manual	First	5.0	13.58	NaN	26.0	1498.0	98.6	honda	city

	Kilometers_Driven	Seats	New_Price	Price	mileage_num	engine_num	power_num	Location_Chennai	Location_Coimbatore	Location_Mumbai	Location_Pune	Year_2010	Year_2011	Year_2012	Year_2013	Year_2015	Fuel_Type_Diesel	Fuel_Type_Petrol	Transmission_Manual	Owner_Type_Second	Brand_audi	Brand_honda	Brand_hyundai	Brand_maruti	Model_a4	Model_creta	Model_ertiga	Model_jazz	Model_wagon	Brand	Model
0	72000.0	5.0	5.51	1.75	26.60	998.0	58.16	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	maruti	wagon
1	41000.0	5.0	16.06	12.50	19.67	1582.0	126.20	0	0	0	1	0	0	0	0	1	1	0	1	0	0	0	1	0	0	1	0	0	0	hyundai	creta
2	46000.0	5.0	8.61	4.50	18.20	1199.0	88.70	1	0	0	0	0	1	0	0	0	0	1	1	0	0	1	0	0	0	0	0	1	0	honda	jazz
3	87000.0	7.0	11.27	6.00	20.77	1248.0	88.76	1	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	maruti	ertiga
4	40670.0	5.0	53.14	17.74	15.20	1968.0	140.80	0	1	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	0	0	audi	a4

		Seats
Brand	Model
ambassador	classic	5.0
audi	a3	5.0
	a4	5.0
	a6	5.0
	a7	5.0
...	...	...
volvo	s60	5.0
	s80	5.0
	v40	5.0
	xc60	5.0
	xc90	7.0

Loading the data set¶

Observations¶

Check for missing values¶

Exploratory Data Analysis¶

Kilometers_Driven¶

Mileage_Num¶

Engine_Num¶

Power_Num¶

Bivariate Data Analysis¶

Correlation Check¶

`Price` vs `Transmission`¶

`Price` vs `Brand`¶

`Price` vs `Fuel_Type`¶

`Price` vs `Owner_Type`¶

Pairplot for relations between numerical variables¶

Missing Value Treatment¶

Normalizing the numerical variables¶

Utility functions¶

Model building¶

Model 0¶

Model 1¶

Model 2¶

Model 3¶

Model 4¶

Model 5¶

Model 6¶

Model 7¶

Model Performance Comparison and Final Model Selection¶

Final Model¶

Business Insights and Recommendations¶

	0
Location	object
Year	int64
Kilometers_Driven	float64
Fuel_Type	object
Transmission	object
Owner_Type	object
Seats	float64
New_Price	float64
Price	float64
mileage_num	float64
engine_num	float64
power_num	float64
Brand	object
Model	object

	Kilometers_Driven	Seats	New_Price	mileage_num	engine_num	power_num	Location_Bangalore	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Hyderabad	Location_Jaipur	Location_Kochi	Location_Kolkata	Location_Mumbai	Location_Pune	Year_1999	Year_2000	Year_2001	Year_2002	Year_2003	Year_2004	Year_2005	Year_2006	Year_2007	Year_2008	Year_2009	Year_2010	Year_2011	Year_2012	Year_2013	Year_2014	Year_2015	Year_2016	Year_2017	Year_2018	Year_2019	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Transmission_Manual	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third	Brand_audi	Brand_bentley	Brand_bmw	Brand_chevrolet	Brand_datsun	Brand_fiat	Brand_force	Brand_ford	Brand_honda	Brand_hyundai	Brand_isuzu	Brand_jaguar	Brand_jeep	Brand_lamborghini	Brand_land	Brand_mahindra	Brand_maruti	Brand_mercedes-benz	Brand_mini	Brand_mitsubishi	Brand_nissan	Brand_porsche	Brand_renault	Brand_skoda	Brand_smart	Brand_tata	Brand_toyota	Brand_volkswagen	Brand_volvo	Model_1000	Model_3	Model_5	Model_6	Model_7	Model_800	Model_a	Model_a-star	Model_a3	Model_a4	Model_a6	Model_a7	Model_a8	Model_accent	Model_accord	Model_alto	Model_amaze	Model_ameo	Model_aspire	Model_aveo	Model_avventura	Model_b	Model_baleno	Model_beat	Model_beetle	Model_bolero	Model_bolt	Model_boxster	Model_br-v	Model_brio	Model_brv	Model_c-class	Model_camry	Model_captiva	Model_captur	Model_cayenne	Model_cayman	Model_cedia	Model_celerio	Model_ciaz	Model_city	Model_civic	Model_cla	Model_classic	Model_cls-class	Model_clubman	Model_compass	Model_continental	Model_cooper	Model_corolla	Model_countryman	Model_cr-v	Model_creta	Model_crosspolo	Model_cruze	Model_d-max	Model_duster	Model_dzire	Model_e	Model_e-class	Model_ecosport	Model_eeco	Model_elantra	Model_elite	Model_endeavour	Model_enjoy	Model_eon	Model_ertiga	Model_esteem	Model_estilo	Model_etios	Model_evalia	Model_f	Model_fabia	Model_fiesta	Model_figo	Model_fluence	Model_fortuner	Model_fortwo	Model_freestyle	Model_fusion	Model_gallardo	Model_getz	Model_gl-class	Model_gla	Model_glc	Model_gle	Model_gls	Model_go	Model_grand	Model_grande	Model_hexa	Model_i10	Model_i20	Model_ignis	Model_ikon	Model_indica	Model_indigo	Model_innova	Model_jazz	Model_jeep	Model_jetta	Model_koleos	Model_kuv	Model_kwid	Model_lancer	Model_laura	Model_linea	Model_lodgy	Model_logan	Model_m-class	Model_manza	Model_micra	Model_mobilio	Model_montero	Model_mustang	Model_mux	Model_nano	Model_new	Model_nexon	Model_nuvosport	Model_octavia	Model_omni	Model_one	Model_optra	Model_outlander	Model_pajero	Model_panamera	Model_passat	Model_petra	Model_platinum	Model_polo	Model_prius	Model_pulse	Model_punto	Model_q3	Model_q5	Model_q7	Model_qualis	Model_quanto	Model_r-class	Model_rapid	Model_redi	Model_redi-go	Model_renault	Model_ritz	Model_rover	Model_rs5	Model_s	Model_s-class	Model_s-cross	Model_s60	Model_s80	Model_safari	Model_sail	Model_santa	Model_santro	Model_scala	Model_scorpio	Model_siena	Model_sl-class	Model_slc	Model_slk-class	Model_sonata	Model_spark	Model_ssangyong	Model_sumo	Model_sunny	Model_superb	Model_swift	Model_sx4	Model_tavera	Model_teana	Model_terrano	Model_thar	Model_tiago	Model_tigor	Model_tiguan	Model_tt	Model_tucson	Model_tuv	Model_v40	Model_vento	Model_venture	Model_verito	Model_verna	Model_versa	Model_vitara	Model_wagon	Model_wr-v	Model_wrv	Model_x-trail	Model_x1	Model_x3	Model_x5	Model_x6	Model_xc60	Model_xc90	Model_xcent	Model_xe	Model_xenon	Model_xf	Model_xj	Model_xuv300	Model_xuv500	Model_xylo	Model_yeti	Model_z4	Model_zen	Model_zest	Brand	Model
2369	56000.0	NaN	7.88	19.5	1061.0	NaN	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	maruti	estilo
5893	51000.0	NaN	7.88	19.5	1061.0	NaN	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	maruti	estilo

	Kilometers_Driven	Seats	New_Price	mileage_num	engine_num	power_num	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Kochi	Year_2013	Year_2014	Year_2017	Year_2018	Fuel_Type_Diesel	Fuel_Type_Petrol	Transmission_Manual	Brand_audi	Brand_hyundai	Brand_maruti	Model_a4	Model_baleno	Model_celerio	Model_eeco	Model_grand
4269	-0.694078	-0.351313	-0.637638	1.136662	-1.034356	-0.841807	0	0	0	1	0	0	0	1	0	1	1	0	0	1	0	0	1	0	0
2025	-0.081329	2.126668	-0.674075	-0.765611	-0.708133	-0.731916	0	1	0	0	1	0	0	0	0	1	1	0	0	1	0	0	0	1	0
5776	-0.469629	-0.351313	1.297640	-0.287665	0.563805	1.136412	1	0	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0
1710	-0.365282	-0.351313	-0.517681	0.732429	-0.706486	-0.545692	0	0	1	0	0	0	1	0	0	1	0	0	0	1	0	1	0	0	0
2363	-0.978527	-0.351313	-0.572951	0.137969	-0.706486	-0.565973	0	1	0	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	1

	# hidden layers	# neurons - hidden layer	activation function - hidden layer	# epochs	batch size	optimizer	time(secs)	Train_loss	Valid_loss	Train_R-squared	Valid_R-squared
0	-	-	-	10	4814	GD	1.697181	116.842415	127.022896	0.077489	0.102093
1	-	-	-	25	4814	GD	3.839952	62.466640	66.866333	0.506804	0.527331

	# hidden layers	# neurons - hidden layer	activation function - hidden layer	# epochs	batch size	optimizer	time(secs)	Train_loss	Valid_loss	Train_R-squared	Valid_R-squared
0	-	-	-	10	4814	GD	1.697181	116.842415	127.022896	0.077489	0.102093
1	-	-	-	25	4814	GD	3.839952	62.466640	66.866333	0.506804	0.527331
2	-	-	-	25	32	SGD	121.499400	25.865023	24.276419	0.795787	0.828393
3	-	-	-	25	64	SGD	57.317932	27.897743	26.422087	0.779738	0.813226
4	1	128	sigmoid	25	64	SGD	74.154647	13.616706	13.191008	0.892491	0.906755
5	1	128	tanh	25	64	SGD	78.145031	8.859550	9.715087	0.930051	0.931325
6	1	128	relu	25	64	SGD	78.647792	9.730357	14.345458	0.923175	0.898594
7	2	[128, 32]	[relu, relu]	25	64	SGD	99.049696	26.398695	19.569878	0.791573	0.861663

	# hidden layers	# neurons - hidden layer	activation function - hidden layer	# epochs	batch size	optimizer	time(secs)	Train_loss	Valid_loss	Train_R-squared	Valid_R-squared
0	-	-	-	10	4814	GD	1.697181	116.842415	127.022896	0.077489	0.102093
1	-	-	-	25	4814	GD	3.839952	62.466640	66.866333	0.506804	0.527331
2	-	-	-	25	32	SGD	121.499400	25.865023	24.276419	0.795787	0.828393
3	-	-	-	25	64	SGD	57.317932	27.897743	26.422087	0.779738	0.813226
4	1	128	sigmoid	25	64	SGD	74.154647	13.616706	13.191008	0.892491	0.906755
5	1	128	tanh	25	64	SGD	78.145031	8.859550	9.715087	0.930051	0.931325
6	1	128	relu	25	64	SGD	78.647792	9.730357	14.345458	0.923175	0.898594
7	2	[128, 32]	[relu, relu]	25	64	SGD	99.049696	26.398695	19.569878	0.791573	0.861663

Loading the data set¶

Observations¶

Check for missing values¶

Exploratory Data Analysis¶

Kilometers_Driven¶

Mileage_Num¶

Engine_Num¶

Power_Num¶

Bivariate Data Analysis¶

Correlation Check¶

Price vs Transmission¶

Price vs Brand¶

Price vs Fuel_Type¶

Price vs Owner_Type¶

Pairplot for relations between numerical variables¶

Missing Value Treatment¶

Normalizing the numerical variables¶

Utility functions¶

Model building¶

Model 0¶

Model 1¶

Model 2¶

Model 3¶

Model 4¶

Model 5¶

Model 6¶

Model 7¶

Model Performance Comparison and Final Model Selection¶

Final Model¶

Business Insights and Recommendations¶

`Price` vs `Transmission`¶

`Price` vs `Brand`¶

`Price` vs `Fuel_Type`¶

`Price` vs `Owner_Type`¶